Searching for Segments Based on an Ontology

ABSTRACT

Aspects described herein relate to identifying media items relevant to a selected subject matter including, for example, determining the subject matter of a first media item, which may comprise at least one of audio content or video content; determining the classification within an ontology of the subject matter of the first media item; analyzing the ontology to identify other subject matter related to the subject matter of the first media item; and performing a search for other media items relevant to the subject matter of the first media item as a function of at least the other related subject matter according to the ontology.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/259,636, filed Sep. 8, 2016, which is a continuation of U.S.application Ser. No. 14/199,063 (now U.S. Pat. No. 9,477,712), filedMar. 6, 2014, which is a continuation of U.S. application Ser. No.12/343,790 (now U.S. Pat. No. 8,713,016), filed on Dec. 24, 2008. Eachof the above-mentioned applications is incorporated herein by referencein its entirety.

FIELD

Aspects described herein relate to a process and apparatus fororganizing segments of audio, video, and media files and determining therelevance of such segments to each other or to a query.

BACKGROUND

Until fairly recently, individuals consumed audio, video, and othermedia content in relatively few forms (television, movies, musicalalbums) from relatively few sources (television stations, movietheaters, radio stations, CDs). However, with the advent of the Internetand an explosion in the availability of low cost electronic consumerproducts, the forms and potential sources of such content have becomemuch more numerous. Today, individuals can consume such content oncomputers at home via the internet, on any number of portable deviceswith memory for storing content, on mobile devices with wireless networkconnectivity to content, on televisions, in movie theaters, etc.Furthermore, the potential sources of audio, video, and multimediacontent are virtually limitless. For instance, subscription-basedtelevision network systems, such as cable television, now provide videoon demand offering in addition to standard broadcast television. Theyalso allow subscribers to record broadcast television programs and watchthem at a time of their own choosing and with the ability to control thecontent stream, such as by fast forward, skip, pause, rewind, etc.

Even further, almost anyone with a computer can now create and widelypublish their own audio, video, and multimedia content on the Internetthrough such outlets as podcasts, videos published via websites such asmyspace.com or youtube.com. Accordingly, both the amount of availablecontent and the specificity of the content have increased dramatically.

As both the volume and specificity of audio, video, and media contentincrease, it is expected that consumers will increasingly consume suchcontent, including television programs, movies, music videos, podcasts,musical albums, and other audio, video, and multimedia assets at thesub-asset level. That is, for instance, rather than watching an entirebaseball game, a consumer may watch only the parts where the team thatbe roots for is at bat or may only watch a highlight reel of the game.In another example, a viewer may view only the light saber fight scenesfrom the Star Wars movie series. In yet other examples, a viewer maywatch only the sports segment or the weather segment of the evening newsprogram or listen to only a single song from a CD or album.

Presently, the only way a consumer of media content can access a segmentof particular interest to that consumer within a media asset is to scanthrough the asset in a linear fashion, such as by using a fast-forwardor rewind function of a media player, to find the desired content.

“Media” refers to the forms in which content may be transmitted.Presently, the most common transmitted media are audio (e.g., music,speech) and visual (photographs, drawings, motion pictures, web pages,animation). These media are typically represented in electronic formats,such as, for example, HTTP, NNTP, UDP, JMS, TCP, MPEG, MP3, wave files,HTML, JPEG, TIFF, and PDF. As transmission technologies become moreadvanced, however, transmitted media will likely involve other sensorydata such as taste, smell and touch.

The decision as to which segments within a complete media item anyindividual wishes to view, of course, is based on the subject matter ofthe content of the segment, hereinafter termed contextual information orsubject matter. “Contextual information” or “subject matter” refersbroadly to the topic or theme of the content and can be virtuallyanything within the realm of human knowledge, such as baseball, strikeout, fast ball, stolen base, mountains, scary, happy, George Carlin,nighttime, cool, winner. The nature and duration of each segment willdepend, of course, on the particular ontology.

Furthermore, as is well-known, advertisers often purchase advertisementtime or space within media assets such as television programs, webpages, podcasts, and radio programs based on the subject matter of themedia. Specifically, advertisers commonly are interested in a particulardemographic of media consumers that can range from the very broad to theextremely narrow. For instance, a producer of beer might be interestedin a demographic of male media consumers aged 18-45, whereas a producerof anti-aging face cream for women might be interested in a demographiccomprising female viewers aged 30-70. The subject matter of a mediaasset often has a very high correlation to a specific demographic.Therefore, the producer of anti-aging face cream may be much moreinterested in placing its advertisement in the middle of a soap operarather than a football competition because the soap opera will be viewedby many more individuals within the demographic that is likely to buyits product than the football competition, even if the footballcompetition has a much larger overall viewing audience than the soapopera.

Thus, not only do individuals expend a significant amount of effortselecting which media assets they consume, but a great deal of effort isexpended by media content providers, (e.g., individual television andradio stations, cable, fiber optic and satellite subscription-basedtelevision network operators, internet service providers), media contentproducers (e.g., television and radio program producers, podcasters,website operators) and advertisers in determining what subject mattersof such media appeal to particular demographics for advertisementplacement and other purposes.

SUMMARY

The invention pertains to methods, systems, and apparatus foridentifying media items relevant to a subject matter of a first mediaitem, the method comprising determining the subject matter of a firstmedia item, the first media item comprising at least one of audiocontent and video content, determining a classification within anontology of the subject matter of the first media item, using theontology to infer other subject matter related to the determined subjectmatter of the first media item, and performing a search for other mediaitems relevant to the determined subject matter of the first media itemas a function of at least the other, related subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a portion of an ontology in accordancewith an embodiment of the present invention.

FIG. 2 is a diagram conceptually illustrating components of a system inaccordance with an embodiment of the present invention.

FIG. 3 is a flow diagram illustrating operation in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

Consumers of media content such as television programs, radio programs,videos, podcasts, digitally recorded music, and web pages willincreasingly desire functionality for finding media content relevant toa particular interest of the consumer, particularly at the sub-assetlevel.

“Media” refers to the forms in which content may be transmitted.Presently, the most common transmitted media are audio (e.g., music,speech) and visual (photographs, drawings, motion pictures, web pages,animation). These media are typically represented in electronic formats,such as, for example, HTTP, NNTP, UDP, JMS, TCP, MPEG, MP3, wave files,HTML, JPEG, TIFF, and PDF. As transmission technologies become moreadvanced, however, transmitted media will likely involve other sensorydata such as taste, smell and touch.

As an example, it is envisioned that media content providers andproducers, such as subscriber-based television network operators (e.g.,cable, satellite and fiber optic television network operators), web siteoperators, podcasters, etc., eventually will offer all or most of themedia content (e.g., television programs, radio programs, videos,digitally recorded music, podcasts, etc.), to consumers on an on-demandbasis (i.e., a consumer can consume any media items at any time of hisor her choosing, rather than having to wait for a particular broadcasttime). This already is the consumption paradigm for most web sites andpodcasters. Furthermore, many subscriber-based television networksalready provide search and/or browse functions that allow theirsubscribers to search for media content. For instance, Video-On-Demand(VOD) is a very popular service offered by many subscription televisionnetworks. Video-On-Demand is a service by which subscribers may chooseprograms from a menu for viewing at a time of each individualsubscriber's choosing. A subscriber simply selects a program for viewingfrom a menu of programs that are available for viewing. The program,which is stored in memory at the headend or another server-side node ofthe network is then streamed to the subscriber's set top box immediatelyfor viewing at that time.

Media items are typically offered by programmers and network operatorsin generally predefined portions herein termed assets. For instance,television programs such as dramas, soap operas, reality shows, andsitcoms are typically broadcast in asset level units known as episodesthat commonly are a half hour or an hour in length (includingadvertisements). Sporting events are broadcast in asset units of asingle game. Music videos are commonly offered in asset unitscorresponding to a complete song or a complete concert performance.

In the television arts, professionals on the business side of the arttend to refer to these as “assets,” whereas professionals on theresearch and technical side of the art more often refer to them as“documents.” In either event, the concept of a media “asset” or“document” is well understood in the industry as well as among contentconsumers (who may not necessarily know the term “document” or “asset,”but know the concept). For instance, a typical television guide printedin a newspaper or the electronic program guides commonly provided by asubscriber-based television network are well known to virtually alltelevision viewers and generally list multimedia content at the assetlevel.

As both the volume and specificity of media content increases, it isexpected that consumers will increasingly consume media at the sub-assetlevel. That is, for instance, rather than watching an entire baseballgame (a media asset), a consumer may watch only the parts where the teamthat he roots for is at bat or may only watch a highlight reel of thegame (a sub-asset level segment). In another example, a viewer may viewonly the light saber fight scenes from the Star Wars movie series.Likewise, advertisers would be interested in buying advertising timewithin television content at the sub-asset level based on the subjectmatter of particular media segments. “Content” refers broadly to theinformation contained in the signal transmitted, and includes, forexample, entertainment, news, and commercials.

A media asset typically can conceptually be broken down into a pluralityof segments at the sub-asset level, each having a cohesive subject ortheme. The nature and duration of each segment will depend, of course,on the particular ontology used for purposes of segmentation as well ason the particular content of each program. For instance, most stageplays and motion pictures readily break down into two or three acts.Each such act can be a different segment. Television programs also canbe segmented according to thematic elements. Certain programs, forinstance, the television news magazine program 60 Minutes can readily besegmented into different news stories. Other programs, however, can besegmented based on more subtle thematic elements. A baseball game can besegmented by inning or at-bats, for instance. A typical James Bond moviecan be segmented into a plurality of action segments, a plurality ofdramatic segments, and a plurality romantic segments. The possibilitiesfor segmentation based on thematic elements is virtually limitless andthese are only the simplest of examples.

Presently, consumers of media can search for media content of interestto them on the Internet through various search engines by entering asearch string including terms that the consumer believes to be relevantto the type of subject matter for which he or she is searching. Suchfunctionality also is available in most subscriber-based televisionnetworks (e.g., cable television, fiber optic, and satellite basedtelevision networks) for searching for television programming. However,in many systems, the search functionality is quite limited as comparedto Internet search engines. For instance, some systems allow onlyliteral title searching.

Even with a robust Internet search engine, the search results often arenot exactly what the consumer was seeking. This can be for severalreasons. First, the consumer may simply have put in a poorly chosensearch string of terms which returns results that are not relevant tothe subject matter for which the consumer is looking. Second, a goodsearch strategy may return results that are relevant to the subjectmatter of interest, but are too numerous to be useful to the consumer.

Furthermore, automatic initiation and/or formulation of searches forcontent relevant to a particular subject matter will become increasinglycommon in the future. Particularly, a media content provider (be it acable, satellite, or fiber optic television network operator, a Web siteoperator, a podcast provider, etc.) may wish to provide a feature to itsusers whereby a consumer can press a button while consuming particularmedia content and be presented with a user interface within which theuser is presented a menu of other content available on the network(preferably at the segment level) having similar or related subjectmatter. For instance, U.S. patent application Ser. No. 12/343,786, filedon Dec. 24, 2008, and entitled “Method and Apparatus for Advertising atthe Sub-Asset Level” and U.S. patent application Ser. No. 12/343,779,filed on Dec. 24, 2008, and entitled “Identification of Segments WithinAudio, Video, and Multimedia Items” and U.S. patent application Ser. No.12/274,452, filed on Nov. 20, 2008, and entitled “Method and Apparatusfor Delivering Video and Video-Related Content at Sub-Asset Level” areall owned by the same assignee as the present application, and are allincorporated herein fully by reference, and discuss various aspects ofone such system.

For instance, above noted U.S. patent application Ser. No. 12/274,452particularly discusses an automated search function that can be offeredto media consumers in the midst of consuming (e.g., viewing) one mediaasset (or segment thereof) that will search for other media items(assets, segments at the sub-asset level, or other items) that arepertinent to the subject matter of the media item being consumed. Moreparticularly, a user of an information network is offered supplementalcontent, the supplemental content being selected based at leastpartially on the subject matter of the media currently being consumed bythe user. “Information network” refers to a collection of devices havinga transport mechanism for exchanging information or content between thedevices. Such networks may have any suitable architecture, including,for example, client-server, 3-tier architecture, N-tier architecture,distributed objects, loose coupling, or tight coupling.

For instance, an exemplary embodiment of such a system might beimplemented as part of the services offered to subscribers of a cabletelevision network or as a feature on a website. Let us consider as anexample an individual consumer who is watching a particular televisionprogram, in this example, a major league baseball game between thePhiladelphia Phillies and the New York Mets. The consumer is permittedat any time during the program to activate a supplemental content searchfeature, such as by depressing a dedicated button on a remote controlunit or mouse clicking on an icon positioned somewhere on a computermonitor. When the feature is thus selected, the set top box (STB), forinstance, sends a signal upstream to a server requesting invocation ofthe feature. In response, the server performs a search for supplementalcontent that pertains to the particular media content being consumed bythat consumer at that time and presents a list of such supplementalcontent to the viewer via a suitable user interface through which theviewer may select one or more for viewing.

Aforementioned U.S. patent application Ser. No. 12/343,786 and U.S.patent application Ser. No. 12/343,779 collectively disclose techniquesand apparatus for segmenting media items (such as media assets) intosmaller segments (such as sub-assets), determining the boundaries andsubject matter of contextually cohesive segments of the items, andclassifying and organizing the segments for searching and browsingaccording to an ontology. The context being referred to in the terms“contextual information” and “contextually cohesive” is the ontologywithin which the subject matter of the media content is beingclassified.

More particularly, U.S. patent application Ser. No. 12/343,779 providesa system for automatically identifying contextually cohesive segments,within media items (e.g., media assets) This task includes bothidentifying the beginnings and ends of such segments as well as thesubject matter of the segments.

As noted above, searching for media content pertinent to a particulartopic, be it a search query manually generated by a user or an automatedsearch for other media items related to the subject matter of a firstmedia item, such as disclosed in aforementioned U.S. patent applicationSer. No. 12/274,452, is an imperfect science. Typically, search enginessearch for content based on key words. In the latter example of anautomated search for additional media items pertinent to the subjectmatter of a first media item, aforementioned U.S. patent applicationSer. Nos. 12/343,786 and 12/343,779 disclose suitable techniques forimplementing such a system. One way of searching such content is todesign a system that determines key words and other subject matter cueswithin the first media item using a number of techniques such asanalyzing the closed caption stream, optical character recognition,video analytics, metadata analysis, etc., and then form a search stringcomprising those keywords for input into a search engine. The searchengine may search for media content on the Internet and/or on a privatenetwork, such as a cable television network. For instance, a cabletelevision service provider may search through its own database oftelevision programming content (e.g., that has previously been analyzedfor subject matter) for other media assets or segments in which thosesame keywords appear. The results might be weighed and ordered as afunction of the number of times the keywords appear, the number ofdifferent keywords that appear and/or other criteria.

The quality of the search results, i.e., the pertinence of the resultsto the subject matter of the first media item, will depend on manyfactors, including, but not limited to, (1) the quality of thedetermination of the subject matter of the first media item, (2) thenumber of keywords that could be identified, (3) whether any of thosekeywords have double meanings, (4) the specificity of the keywords, and(5) the quantity of other media items having those keywords therein.

As previously noted, some or all of the results of such a search may notbe particularly pertinent to the consumer's interests.

The mere matching of keywords often will not find all of the relevantcontent or the most relevant content. Particularly, depending on theparticular user's interests, segments having very different keywordsactually may be closely related depending on the user's viewpoint. Theinverse also is true. That is, the identical word may have verydifferent and unrelated meanings in different contexts.

Let us consider an example to illustrate the concept. Suppose a vieweris watching a football game, and particularly the Super Bowl game of2008 between the New England Patriots and the New York Giants. Let usalso assume that a play occurs in which the quarterback for the NewEngland Patriots, Eli Manning, throws an interception. During orimmediately after this play, the viewer activates the automated searchfunction with the hope of viewing other plays in which the New EnglandPatriots offensive team turned over the ball to the other team. Asanyone with a robust knowledge of the game of football will know, aninterception is only one of several ways in which a turnover can occurin a football game. Another way is a fumble. Yet another way is asafety. The above example illustrates an “is a” relationship in thenature of a simple classification system or taxonomy. However, manyother important relationships between concepts can be represented in arobust ontology. Such relationship may include “belongs to,” “is a partof,” and “may include.” For instance, a tipped pass often leads to aninterception or a near interception (a pass that almost wasintercepted). It also allows otherwise ineligible receivers to receive apass. Accordingly, plays in which a tipped pass occurs and/or anineligible receiver catches a pass may be a near interception and,therefore, highly relevant to another play that is an interception.Thus, as a practical example, it is quite likely that a contextualanalysis of the play in which the interception occurred may yield thekeywords “New England Patriots,” “Eli Manning,” and “interception.” Asearch using these keywords is likely to miss turnovers that occurred asthe result of a fumble or safety and near interceptions.

As can be seen from this example, a robust knowledge of football wouldenable the formulation of a better search for pertinent content. Thus,if the viewer had a robust knowledge of football and entered his ownsearch string, he may have thought of adding the words “fumble,”“safety,” “tipped pass” and/or “ineligible receiver” to the searchstring or of including the term “turnover” in addition to or instead of“interception.”

The present invention offers a way to improve searches by capitalizingon a robust knowledge of subject matter in connection with specificknowledge domains.

Particularly, aforementioned U.S. patent application Ser. Nos.12/274,452, 12/343,786 and, 12/343,779 disclose aspects of an exemplarysystem within which the present invention can be incorporated. In any ofthe exemplary systems discussed in one or more of these patents, mediaitems (e.g., assets) are partitioned into segments (e.g., sub-assets)having cohesive subject matter. The segments (or at least informationabout the segments) are stored in a database. The database stores eachsegment's identity (such as by the identity of the media asset of whichit forms a part and the time indexes within that asset of the start andend times of the segment) and its contextual information, e.g., in theform of a plurality of attribute/value pairs, a flat (table) databasemodel of tuples (an ordered list of values), hierarchical data models,or relational data models). For example, an attribute for a segmentcomprising an at-bat of a baseball game may be “Player at Bat” and thevalue may be “Jimmie Rollins”.

In order to develop such a database, an ontology (or classificationsystem) 202 is developed to provide a defined framework for classifyingmedia segments by subject matter. An ontology essentially is a formalrepresentation of a set of concepts within a domain and therelationships between those concepts. It is used to reason about theproperties of that domain, and may be used to define the domain. Keyelements of an ontology include:

-   -   Classes: sets, collections, concepts, types of objects, or kinds        of things    -   Attributes: aspects, properties, features, characteristics, or        parameters that objects (and classes) can have    -   Relations: ways in which classes and individuals can be related        to one another    -   Restrictions: formally stated descriptions of what must be true        in order for some assertion to be accepted as input    -   Rules: statements in the form of an if-then        (antecedent-consequent) sentence that describe the logical        inferences that can be drawn from an assertion in a particular        form

Thus, for instance, “an interception is a turnover” is a relationship.Also, “an interception may happen on a tipped pass” also is arelationship. An example of a restriction is “non-eligible receivers cancatch a pass only if it is tipped by a defender”. An example of a ruleis “plays involving ineligible receivers may be near interceptions” and,therefore, may be closely related to an interception.

The segments are then indexed with respect to the ontology. The databasecan then be searched such as by a keyword search and/or the ontology canbe examined for segments relevant to any desired subject matter (i.e.,faceted searching). Furthermore, as discussed in detail inaforementioned U.S. patent application Ser. No. 12/343,779, preferablydifferent portions of the ontology related to different knowledgedomains are specifically designed as a function of those specificknowledge domains, thus making the ontology even more robust.

A knowledge domain essentially is a high level theme or subject. As usedherein, “knowledge domain” refers to a relatively broad category oftheme or subject, such as baseball, football, romance, Spanish, music,medicine, law, comedy. The breadth and subject of any knowledge domainwithin the ontology is entirely within the discretion of its creator.The only requirement is that a knowledge domain have sub-categories ofsubject matter.

The present invention leverages the robust knowledge of subject matterinherently contained in the ontology to provide an improved way oflocating media content relevant to a search or another piece of mediacontent. Particularly, the ontology, and particularly, the knowledgedomain specific portions of an ontology of a system such as disclosed inaforementioned U.S. patent application Ser. No. 12/343,779 inherentlyhave built-in to them a robust knowledge of various knowledge domains.Accordingly, a process of formulating query search strings incorporatingthe robust knowledge provided by the ontology is likely to substantiallyimprove the quality of the search results, i.e., return morecontextually pertinent results, in most cases.

Indexing segments according to an ontology that has been developed withrobust knowledge of the particular knowledge domain of interest, (e.g.,football) would disclose the relatedness of the concept of aninterception to the concepts of a fumble, a turnover, tipped pass,ineligible receiver, and a safety. Thus, designing a search engine thattakes into account related concepts as well as the degree of relatednessin accordance with the structure of the ontology could provide muchbetter results than a conventional search engine that does not take intoaccount the robust knowledge inherent in the ontology, particularly theknowledge domain specific portions of the ontology.

Thus, for instance, in the example above, the ontology for football mayinclude portions such as illustrated in FIG. 1. As can be seen, oneportion of the ontology includes a category called “turnovers” and showsthat it has subcategories “interception”, “fumble”, and “safety” (an “isa” relationship in the nature of a simple taxonomy). This type ofrelationship is visually represented in FIG. 1 by solid lines and a treestructure. The ontology also reveals, for instance, that interceptionmay happen on a tipped pass (the “may happen” relationship beingrepresented by a dashed line). Many other relationship types, rules,restrictions and attributes also may be represented in the ontology.Thus, if a subject matter analysis of the play yields only the word“interception” as a keyword, consultation of the ontology discloses thatconcept of an “interception” is a type of “turnover” and that theconcepts of a “fumble” and a “safety” are sister concepts to the conceptof an “interception” because all three are forms of a “turnover.” Thus,the search query can be modified to include one or more of “turnover”,“fumble”, and “safety” as keywords in addition to “interception.” Theterms “fumble” and “safety” may be weighed lower than the terms“interception” and/or “turnover” so that interceptions are weighted moreheavily than fumbles and safeties (since they, obviously, are moreclosely related to the play in question than a fumble or a safety).

FIG. 2 is a block diagram illustrating conceptually the components of anexemplary system 200 incorporating the present invention. A collectionof multimedia files (e.g., media assets) 201 exists that can bepartitioned into coherent segments (e.g., at the sub-asset level)according to an ontology 202. The segments will be maintained in asegment database 203 that identifies the segments and their subjectmatter. The identification data for each segment may include, forinstance, the identification of the asset of which it forms a part andthe time indexes within the asset of the start and end times of theparticular segment. The subject matter information may comprisevirtually any information about the subject of the segment. The subjectmatter information in the segment database 203 may be stored as one ormore attribute/value pairs. Thus, using as an example, a segmentcomprising a single play in a football competition, one of theattributes may be “Key Offensive Players” and its value would beassigned the names (or other identification indicia) of the primaryoffense team players involved in the play.

The ontology as well as the number of attributes and the specificattributes for any given segment can differ as a function of theparticular knowledge domain of the asset from which the segment istaken. More specifically, just as mentioned above with respect to theontology, the particular pieces of contextual information maintained inthe database may be specific to the knowledge domain of the media itemsbeing segmented. Preferably, the specific knowledge domain is selectedas a function of the knowledge domain of the media asset or item that isbeing segmented. For instance, the attributes stored in connection witha segment that forms part of a football competition may be differentthan the attributes that are stored for a segment that is part of abaseball competition, which are even further different than theattributes that are stored in connection with a segment that is part ofa program about cooking.

Generally, the knowledge domain of most media items is either known inadvance of any subject matter analysis of the item (e.g., from the titleof the asset) or is easily determinable via an initial subject matteranalysis. The knowledge domain of the item may be input manually by ahuman operator. Alternately, it may be derived by analysis of the titleof the asset. This can be done, for instance, by keyword analysis withinthe title or by comparing the title against a database of known programtitles correlated to their knowledge domains. In any event, once theknowledge domain of the media item is determined (e.g., football,baseball, sitcom, reality show, reality competition, game show, etc.),the specific pieces of information determined and stored with respect toa segment (i.e., the attribute/value pairs stored in the segmentdatabase 203) also can be customized as a function of the specificknowledge domain of the item of which it forms a part (or the predictedinterests of a particular demographic).

Thus, for instance, continuing with the football competition example,the attributes for segments of a football competition may include TeamOn Offense, Team On Defense, Game Time, Down Number, Key OffensivePlayers, Key Defensive Players, Type of Play (e.g., kick off, pointafter attempt, punt regular play), Yards Gained/Lost, etc.

On the other hand, the attributes for segments forming a portion of abaseball competition may be substantially different.

In short, the attributes that are to be stored in the database for agiven segment may differ depending on the knowledge domain of the assetfrom which the segment is taken. Specialized attribute sets may bedesigned for the most common, relevant or popular knowledge domains forthe given population of media assets to be segmented.

Thus, in a preferred embodiment of the invention, a plurality 205 ofdifferent subject matter gathering processes 206-213 are utilized todetermine the boundaries and subject matter of cohesive segments of themedia assets 201.

The process of identifying contextually cohesive segments of multimediaassets segmentation process 205 has at least two parts, namely, (1)identifying cohesive, meaningful segments within media items (e.g.,identifying the beginning and end of a meaningful segment having acohesive theme or subject) and (2) identifying that subject.Particularly, identifying keywords or other thematic elements in amultimedia file in order to identify subject matter is only half thebattle. Delimiting the segments, i.e., determining the boundaries(beginning and end) of a cohesive segment is an additional complexity.

Various technologies, generally represented within segmentor 205 in FIG.2 may be utilized for determining the subject matter of media items,such as assets, and partitioning them into coherent segments as afunction of their subject matter.

Many technologies are available now that can be adapted for use foridentifying media segments either as stand-alone components or incombination within the present invention. For instance, software 206 isnow available that can capture the closed caption stream within a mediaasset and analyze it for subject matter. Further, software 207 isavailable that can analyze the audio portion of a multimedia stream anddetect speech within the audio stream and convert the speech to text(which can further be analyzed for subject matter, just like the closedcaption stream).

In fact, voice recognition software can be used to detect the identityof a particular speaker within a media stream. For instance, certaintypes of multimedia files, such as television programs of a particulartitle (e.g., “60 Minutes” or “Seinfeld”) have a known set of individualsthat are likely to speak during the program. In 60 Minutes, forinstance, it would be the handful of reporters that regularly hostsegments of the program. In “Seinfeld”, it would be one of the handfulof main characters—Jerry Seinfeld (played by actor Jerry Seinfeld),Elaine Benes played by actor Julia Louis-Dreyfus), Cosmo Kramer (playedby actor Michael Richards), and George Costanza (played by actor JasonAlexander). Such software can be pre-programmed to recognize the voicesof those main characters/actors and then used to recognize those voicesto provide even richer subject matter data.

Additionally, audio analytics software 208 is now available that cananalyze the non-speech aspects of the audio stream of an audio ormultimedia file to determine additional subject matter information fromsounds other than speech. For instance, such software can detect,recognize, and distinguish between, for instance, the sound of a crowdcheering or booing, sounds associated with being outdoors in a naturalsetting or being outdoors in an urban setting, or being indoors in afactory or an office or a residence, etc. For example, U.S. Pat. No.7,177,881 discloses suitable software for detecting semantic events inan audio stream.

Even further, optical character recognition software 209 can be used todetermine text that appears in a scene. See, e.g. Li, Y. et al.“Reliable Video Clock Recognition,” Pattern Recognition, 2006, 1CPR2006, 18th International Conference on Pattern Recognition. Suchsoftware can be used, for instance, to detect the clock in a timedsporting event. Specifically, knowledge of the game time could be usefulin helping determine the nature of a scene. For instance, whether theclock is running or not could be informative as to whether the ball isin play during a football game. Furthermore, certain times during asporting event are particularly important, such as two minutes beforethe end of a professional football game. Likewise, optical characterrecognition can be used to determine the names of the actors,characters, and/or other significant persons in a television program orthe like simply by reading the credits at the beginning and/or end ofthe program.

Furthermore, video analytics software 210 is available that can analyzeother visual content of a video or multimedia stream to determinesubject matter information, e.g., indoors or outdoors, presence orabsence of cars and other vehicles, presence or absence of human beings,presence or absence of non-human animals, etc. In fact, software isavailable today that can be used to actually recognize specificindividuals by analyzing their faces.

Even further, there may be significant metadata contained in amultimedia stream. While a closed captioning stream may be consideredmetadata, we here refer to additional information. Particularly, themakers or distributors of television programs or third party providerssometimes insert metadata into the stream that might be useful indetermining the subject matter of a program or of a portions of aprogram. Such metadata may include almost any relevant information, suchas actors in a scene, timestamps identifying the beginnings and ends ofvarious segments within a program, the names of the teams in a sportingevent, the date and time that the sports event actually occurred, thenumber of the game within a complete season, etc. Accordingly, thesegmentor 205 also may include software 211 for analyzing such metadata.

Even further, companies now exist that provide the services ofgenerating and selling data about sporting events, television programs,and other events. For instance, Stats, Inc. of Northbrook, Ill., USAsells such metadata about sporting events. Thus, taking a baseball gameas an example, the data may include, for instance, the time that eachhalf inning commenced and ended, data for each at bat during the game,such as the identity of the batter, the result of the at-bat, the timesat which the at-bat commenced and ended, the statistics of each playerin the game, the score of the game at any given instance, the teamsplaying the game, etc. Accordingly, another software module 212 can beprovided to analyze data obtained or otherwise obtained from externalsources, such as Stats, Inc.

Furthermore, the aforementioned optical character recognition (OCR) ofthe game clock in a sporting event also would be very useful in terms ofaligning the game time with the media stream time. For instance,external data available from sources such as Stats, Inc. includes datadisclosing the time during the game that certain events (e.g., plays)occurred, but generally does not contain any information correlating thegame time to the media stream time index. Thus, an alignment algorithm121 for correlating game time with data stream time also would be auseful software component for purposes of identifying cohesive segmentsin connection with at least certain types of multimedia content, such astimed sports competitions.

Furthermore, external data is widely available free of charge. Forinstance, additional subject matter information may be obtained via theInternet. Particularly, much information about sporting events andtelevision shows is widely available on the Internet from any number offree sources. For instance, synopses of episodes of many televisionshows are widely available on the Internet, including character andactor lists, dates of first airing, episode numbers in the sequence ofepisodes, etc.).

The present invention may rely on any or all of these techniques fordetermining the subject matter of a media item as well as the beginningand end of coherent segments corresponding to a particular subjectmatter. Also, as previously noted, different subject matter informationgathering processes for different knowledge domains may use differentsets of these tools and/or use them in different ways or combinations.Furthermore, as previously mentioned, the same technologies in segmentor205 may be used to determine the knowledge domains (i.e., the moregeneral subject matter) of assets in embodiments in which suchinformation is not predetermined so that the system can choose theparticular set of technologies and particular attribute/value setsadapted to that knowledge domain for carrying out the segmentation.

It should be noted that the classification of media items need not beexclusive. For instance, a given segment may be properly assigned two ormore relatively disparate contextual information within the ontology.For instance, a television program on the History Channel having aportion pertaining to the origination of the sport of golf in Scotlandmay be classified as pertaining to all of (1) history, (2) travel, and(3) sports.

It should be understood, that the example above is simplified forpurposes of illustrating the proposition being discussed. In actuality,of course, a segment about the history and origins of golf in Scotlandwould be classified and sub-classified to multiple levels according toan ontology. For instance, in a robust ontology, this segment would notbe merely classified under history, but probably would be furthersub-classified under European history, and even further sub-classifiedunder Scottish history, etc. It would further be classified not merelyunder travel, but probably under travel, then sub-classified underEuropean travel, and then even further sub-classified under Scottishtravel, etc. Finally, it also would not merely be classified undersports, but, for instance, under sports and further sub-classified undersolo sports, and even further sub-classified under golf.

The segmentation also need not necessarily be discrete. Segments alsomay overlap. For instance, the same show on the History Channelmentioned above may start with a segment on Scottish history thatevolves into a segment on the origins of golf and that even furtherevolves into a segment on Scottish dance music. Accordingly, a firstsegment may be defined as starting at timestamp 5 minutes:11 seconds inthe program and ending at timestamp 9m:18s classified underHistory:European:Scotland. A second segment starting at 7m:39s andending at 11m:52s may be classified under Sports:Solo:Golf and a thirdsegment starting at 11m:13s and ending at 14m:09s may be classifiedunder Music:Dance:Scottish. In this example, the various segmentsoverlap each other in time.

Even further, a segment can be any length, including zero (i.e., it is asingle instant in time within the media item).

The ones of the various information gathering processes used to analyzea particular media item and the manner of their use may be customized asa function of the knowledge domain of the particular item. The systemoperator may predetermine a plurality of subject matter informationgathering processes, each adapted to a particularly relevant, popular,or common knowledge domain for assets within its collection of mediaassets and/or are popular interests among the expected users of thesystem (e.g., subscribers of a television service network employing thesystem or advertisers on that television network). A more generic,default information gathering process can be used for media items whoseknowledge domain either cannot reasonably be determined or that do notfall into any of the other knowledge domain customized processes.

For instance, if the present invention is to be implemented on asubscription-based television service network, then the plurality ofknowledge domains to which the ontology, subject matter informationgathering processes, and/or attribute sets are customized should bespecifically adapted for the types of media assets that commonlycomprise television programming. For instance, the vast majority ofnetwork television programs fall in to one of a relatively small numberof categories or knowledge domains. For instance, probably the vastmajority of programs made for television fall into one of the followingdomains: news and current events, situational comedies, law-baseddramas, police-based dramas, medical-based dramas, reality TV, realitycompetitions, sports competitions (which might further be broken downinto a handful of the most popular sports, such as football, hockey,baseball, basketball, soccer, golf), children's cartoons, daytime soapoperas, educational or informative (history, travel, technology), sketchcomedy, talk shows, and game shows.

Hence, a specialized portion of the ontology, a specialized set ofattributes, and/or a specialized subject matter information gatheringprocess can be developed and used for each of these knowledge domains.

Once the segments are determined and the subject matter information hasbeen gathered, the segments are then stored in the segment database 203with all of their applicable attribute/value pairs.

It should be understood that the media assets themselves do notnecessarily need to be physically separated into distinct files at thesegment level in segment database 203. For instance, segment database203 may merely comprise data identifying the segments.

The segments also are indexed in accordance with the ontology 202.Again, if the overall system is to be used for a specific type of media,e.g., a television programming, then the overall ontology preferably isspecifically adapted to classifying that type, e.g., multimedia itemscommon to television programming. Furthermore, as previously noted,distinct portions of the ontology pertaining to different knowledgedomains within television programming content may be specificallyadapted to those knowledge domains.

In addition to simply populating the segment database 203 with the datafor a plurality of media segments, those segments also are indexed underthe ontology, i.e., the ontology is populated with the segments, asillustrated in oval 215. Finally, as illustrated by oval 216, theontology can now be used to determine the relevance of any media segmentclassified therein to any other media segment classified therein notmerely by classification (e.g., an “is a” relationship), but by anynumber of relations.

Using the ontology to identify related concepts and keywords, animproved search algorithm can be developed in connection with aparticular subject matter by incorporating such related concepts and/orkeywords in the searching algorithm.

In accordance with a very simple embodiment of the present invention,for instance, the searching algorithm may simply develop a search string(a set of words) to plug into a pre-existing search engine. In thissimple embodiment, the system will identify keywords derived from thesubject matter of the media content using the tools discussed above.Then the ontology is consulted to identify related concepts and/orkeywords. For instance, in the football example above, the ontologywould disclose that an “interception” is a subcategory or child conceptof the concept/keyword “turnover”. It also would disclose that sisterforms of turnover to an “interception” include “fumbles” and “safeties”.The algorithm may also go down one level in the ontology to determinechild concepts/keywords within the category of “interception” that maybe useful search terms to add to the search string. Even further, thealgorithm may look for other relations, rules, and/or restrictions, suchas “interception” “may happen” on a “tipped pass,” etc.

In a slightly more complex embodiment, the various concepts/keywordsdeveloped through analysis of the ontology may be given differentweights in terms of finding relevant documents. For instance, in asimple embodiment of this feature, those concepts/keywords not founddirectly through the subject matter analysis of the asset or segmentbeing consumed, but identified as related concepts via the ontology maybe given a lower weight in the search algorithm. In more complexembodiments, concepts from one level above may be weighed differentlythan sister concepts within the same level, which may be weigheddifferently than child concepts/keywords found by looking down one levelin the ontology.

Of course, the present invention is not limited to simply identifyingrelated keywords for insertion into a search string. The presentinvention can be utilized to identify related concepts/keywords withinthe ontology and then incorporate those concepts/keywords in any type ofsearching algorithm, not just keyword searching.

Another way to use the present invention in connection with a systemsuch as the system disclosed in U.S. patent application Ser. No.12/274,452 for automated searching for media content having subjectmatter similar to the subject matter of a particular media piece is todirectly use the ontology to find other media content that is indexedwithin the ontology similarly to the indexing of the particular piece.

FIG. 3 is a flowchart illustrating process flow in accordance with theprinciples of the present invention in an exemplary embodiment of anautomated search feature such as the one disclosed in aforementionedU.S. patent application Ser. No. 12/274,452. In accordance with thatflow, in step 301, an ontology is created specifically adapted to aspecific type of media content. The overall ontology is adapted to itsparticular task, e.g., a subscription-based television service providerwould adapt its ontology for multimedia content, and specifically thetypical television programming type of multimedia content. On the otherhand, a music service provider, e.g., an online radio station, might usea very different ontology specifically adapted for audio content, andmore specifically music content. Furthermore, the ontology preferably isdeveloped with different portions thereof based on different knowledgedomains, each particularly adapted to a different specific knowledgedomain. Again, using the television network service provider as anexample, the ontology can have within it different portions specificallyadapted for sports games, cooking shows, situation comedies, realityshows, game shows, etc. If the larger domain is music, the specificknowledge domains might instead be rock, jazz, rhythm & blues andclassical.

In any event, next, in step 302, a database is built storing the subjectmatter information for a plurality of media items. Furthermore, theontology is populated with the media items, i.e., the various mediaitems are classified within the ontology. The subject matter informationand classification information for the library of media items can becollected, for instance, in the ways described hereinabove, includingspeech recognition analysis, OCR analysis, closed caption analysis,metadata analysis, audio analytics, video analytics, external data, etc.

Next, in step 303, the media item that is currently being consumed isanalyzed using any or all of the various aforementioned technologies todetermine the subject matter of that media item. Next, in step 304, theitem being consumed is classified within the ontology. In step 305, theontology is analyzed to determine additional concepts and/or keywordsrelevant to the subject matter information for the media item beingconsumed. Next, in step 306, a search is formulated for other mediaitems having similar subject matter information to the item beingconsumed using the subject matter information collected directly fromthe media item as well as the related concepts and key words derivedfrom the analysis of the ontology.

Finally, in step 307, the search is performed and the viewer ispresented with the results of the search.

It should be understood that the exemplary embodiments disclosed hereinin connection with automated searching for related content to a mediaitem being consumed by a viewer is merely exemplary. The concepts of thepresent invention can be applied in many other contexts. For instance,it can be applied to a search, such as an Internet search, developed bya human user when there is an available ontology relevant to theknowledge domain of the search. Particularly, the search terms orkeywords input into a search engine by a human user can be run throughthat ontology to identify additional related terms according to theontology and then an enhanced search string can be developed for use inperforming the actual search (with or without making the human useraware of the modification).

In yet another embodiment, the ontology can be used to determine therelevance to each other of any two or more media segments alreadyclassified within the ontology. This embodiment might be useful inconnection with tasks such as automatically generating playlistspertaining to a particular subject matter.

Even further, it is not even necessary that any content be previouslyindexed within the ontology. An ontology itself (completely devoid ofanything actually being indexed thereunder) would still provide robustinformation as to the relatedness of concepts within the ontology.Therefore, an ontology can be used to help improve the parameters usedto search for content relevant to any particular topic within theknowledge domain of the ontology in the complete absence of any contentactually being indexed under the ontology. The existence of contentindexed actually under the ontology would make it easier to locate andidentify relevant content since the act of identifying other relatedconcepts within the ontology would inherently also identify thecorresponding content indexed under those related concepts in theontology.

Furthermore, it should be understood by those of skill in the art that,while most of the embodiments discussed hereinabove use exemplary mediaunits of segments at the sub-asset level, this is merely exemplary. Thepresent invention can be used in connection with the classification,searching, and identifying of media items in units of any size (bothphysically and conceptually), including the sub-asset level, the assetlevel, or any other units.

In at least one preferred embodiment of the invention, all of the mediaitems 201 are stored in a digital memory as digital files. The ontology202 and the segment database 203 also are stored in a computer or otherdigital memory. The various subject matter information gathering modulesare preferably implemented as software routines running on a general orspecial purpose digital processing device. However, the processes alsocould be implemented in any of a number of other reasonable manners,including, but not limited to, integrated circuits, combinational logiccircuits, field programmable gate arrays, analog circuitry,microprocessors, state machines, and/or combinations of any of theabove. The mechanisms for formulating the search strategy as well as themechanism for performing the search also are preferably implemented assoftware routines, but also could be implemented in any of theaforementioned manners.

By designing the ontology, the subject matter information gatheringprocess and/or the attribute/value pairs for the segment database 203particularly for a plurality of different specific knowledge domainsbased on a robust knowledge of each such knowledge domain (e.g.,cooking, football, sitcoms, law dramas), one can provide much richer andmore robust search and retrieval functionality for users.

The ontology 202 can be continuously refined as types of programming,products, services, demographics, etc. are developed or themselvesbecome more refined.

Having thus described a few particular embodiments of the invention,various alterations, modifications, and improvements will readily occurto those skilled in the art. Such alterations, modifications, andimprovements as are made obvious by this disclosure are intended to bepart of this description though not expressly stated herein, and areintended to be within the spirit and scope of the invention.Accordingly, the foregoing description is by way of example only, andnot limiting. The invention is limited only as defined in the followingclaims and equivalents thereto.

1. An apparatus comprising: one or more processors; and memory storinginstructions that, when executed by the one or more processors, causethe apparatus to: determine, based on descriptive information associatedwith first audiovisual content, one or more first words; determine oneor more second words based on: the one or more first words; and one ormore relationships that associate at least one of the one or more firstwords with the one or more second words; determine search informationthat is based on the one or more first words, the one or more secondwords, a first weight associated with at least one of the one or morefirst words, and a second weight associated with at least one of the oneor more second words; determine, based on the search information, secondaudiovisual content; and cause display of information associated withthe second audiovisual content.
 2. The apparatus of claim 1, wherein theinstructions, when executed by the one or more processors, further causethe apparatus to: determine the one or more first words by determining,based on receipt of a signal indicating a user selection of a searchfunction, the one or more first words.
 3. The apparatus of claim 1,wherein the instructions, when executed by the one or more processors,further cause the apparatus to: determine the one or more first words bydetermining, based on determining that the first audiovisual content isbeing output for display, the one or more first words.
 4. The apparatusof claim 1, wherein the instructions, when executed by the one or moreprocessors, further cause the apparatus to: determine the one or morefirst words by performing facial recognition or voice recognition on thefirst audiovisual content.
 5. The apparatus of claim 1, wherein theinstructions, when executed by the one or more processors, further causethe apparatus to: determine the second audiovisual content by searchingfor one or more matching audiovisual content segments that have anattribute matching at least one of the one or more first words or atleast one of the one or more second words.
 6. The apparatus of claim 1,wherein a first relationship of the one or more relationships comprisesa parent-child relationship between the one or more first words and theone or more second words, and wherein a difference between the firstweight and the second weight is dependent on the parent-childrelationship.
 7. The apparatus of claim 1, wherein a first relationshipof the one or more relationships associates the one or more second wordsas a subcategory of the one or more first words.
 8. The apparatus ofclaim 1, wherein a first relationship of the one or more relationshipscomprises a sibling relationship between the one or more first words andthe one or more second words.
 9. The apparatus of claim 1, wherein theinstructions, when executed by the one or more processors, further causethe apparatus to: select, based on a knowledge domain for the firstaudiovisual content, a process for determining the descriptiveinformation; and perform the process for determining the descriptiveinformation by analyzing video or audio of the first audiovisualcontent.
 10. The apparatus of claim 9, wherein the second weight isbased on a degree of relatedness, in the knowledge domain, between theone or more first words and the one or more second words.
 11. Theapparatus of claim 1, wherein the one or more relationships are definedin an ontology.
 12. The apparatus of claim 1, wherein the firstaudiovisual content comprises television programming.
 13. The apparatusof claim 1, wherein the descriptive information associated with thefirst audiovisual content comprises optical character recognitioninformation.
 14. The apparatus of claim 1, wherein the first weight ishigher than the second weight.
 15. The apparatus of claim 1, wherein afirst relationship of the one or more relationships comprises aparent-child relationship between the one or more first words and theone or more second words, and wherein the first weight is lower than thesecond weight.
 16. The apparatus of claim 1, wherein a firstrelationship of the one or more relationships comprises a siblingrelationship between the one or more first words and the one or moresecond words, and wherein the first weight is lower than the secondweight.
 17. The apparatus of claim 1, wherein the instructions, whenexecuted by the one or more processors, further cause the apparatus to:determine the one or more first words by determining, based on a closedcaption stream associated with the first audiovisual content, the one ormore first words.
 18. The apparatus of claim 1, wherein theinstructions, when executed by the one or more processors, further causethe apparatus to: determine the one or more first words by performingvideo analytics on the first audiovisual content.
 19. The apparatus ofclaim 1, wherein the instructions, when executed by the one or moreprocessors, further cause the apparatus to: determine the one or morefirst words by determining, based on metadata associated with the firstaudiovisual content, the one or more first words.
 20. An apparatuscomprising: one or more processors; and memory storing instructionsthat, when executed by the one or more processors, cause the apparatusto: determine, based on first audiovisual content being output fordisplay, one or more first words; determine one or more second wordsbased on: the one or more first words; and one or more relationshipsthat associate at least one of the one or more first words with the oneor more second words; determine search information that is based on theone or more first words and the one or more second words; determine,based on the search information, second audiovisual content; and causedisplay of information associated with the second audiovisual content.21. The apparatus of claim 20, wherein the instructions, when executedby the one or more processors, further cause the apparatus to: determinethe one or more first words by determining, based on receipt of a signalindicating a user selection of a search function, the one or more firstwords.
 22. The apparatus of claim 20, wherein the instructions, whenexecuted by the one or more processors, further cause the apparatus to:determine the one or more first words by performing facial recognitionor voice recognition on the first audiovisual content.
 23. The apparatusof claim 20, wherein a first relationship of the one or morerelationships comprises a parent-child relationship between the one ormore first words and the one or more second words, and wherein theinstructions, when executed by the one or more processors, further causethe apparatus to: determine a first weight associated with the one ormore first words and a second weight associated with the one or moresecond words, and wherein a difference between the first weight and thesecond weight is dependent on the parent-child relationship.
 24. Theapparatus of claim 23, wherein the second weight is based on a degree ofrelatedness, in a knowledge domain, between the one or more first wordsand the one or more second words.
 25. The apparatus of claim 20, whereinthe instructions, when executed by the one or more processors, furthercause the apparatus to: determine the one or more first words bydetermining, based on descriptive information associated with the firstaudiovisual content, the one or more first words.
 26. The apparatus ofclaim 20, wherein the one or more relationships associate the one ormore second words as a subcategory of the one or more first words. 27.The apparatus of claim 20, wherein the one or more relationshipscomprise a sibling relationship between the one or more first words andthe one or more second words.
 28. One or more non-transitory computerreadable media storing instructions that, when executed, cause:determining, based on descriptive information associated with firstaudiovisual content, one or more first words; determining one or moresecond words based on: the one or more first words; and one or morerelationships that associate at least one of the one or more first wordswith the one or more second words; determining search information thatis based on the one or more first words, the one or more second words, afirst weight associated with at least one of the one or more firstwords, and a second weight associated with at least one of the one ormore second words; determining, based on the search information, secondaudiovisual content; and causing display of information associated withthe second audiovisual content.
 29. The one or more non-transitorycomputer readable media of claim 28, wherein the instructions, whenexecuted, cause the determining the one or more first words by causingdetermining, based on receipt of a signal indicating a user selection ofa search function, the one or more first words.
 30. The one or morenon-transitory computer readable media of claim 28, wherein theinstructions, when executed, cause the determining the one or more firstwords by causing determining, based on determining that the firstaudiovisual content is being output for display, the one or more firstwords.
 31. The one or more non-transitory computer readable media ofclaim 28, wherein the instructions, when executed, cause the determiningthe one or more first words by causing performing facial recognition orvoice recognition on the first audiovisual content.
 32. The one or morenon-transitory computer readable media of claim 28, wherein theinstructions, when executed, cause the determining the secondaudiovisual content by causing searching for one or more matchingaudiovisual content segments that have an attribute matching at leastone of the one or more first words or at least one of the one or moresecond words.
 33. The one or more non-transitory computer readable mediaof claim 28, wherein a first relationship of the one or morerelationships comprises a parent-child relationship between the one ormore first words and the one or more second words, and wherein adifference between the first weight and the second weight is dependenton the parent-child relationship.
 34. The one or more non-transitorycomputer readable media of claim 28, wherein a first relationship of theone or more relationships associates the one or more second words as asubcategory of the one or more first words.
 35. The one or morenon-transitory computer readable media of claim 28, wherein a firstrelationship of the one or more relationships comprises a siblingrelationship between the one or more first words and the one or moresecond words.
 36. The one or more non-transitory computer readable mediaof claim 28, wherein the instructions, when executed, cause: selecting,based on a knowledge domain for the first audiovisual content, a processfor determining the descriptive information; and performing the processfor determining the descriptive information by analyzing video or audioof the first audiovisual content.
 37. The one or more non-transitorycomputer readable media of claim 36, wherein the second weight is basedon a degree of relatedness, in the knowledge domain, between the one ormore first words and the one or more second words.
 38. The one or morenon-transitory computer readable media of claim 28, wherein the one ormore relationships are defined in an ontology.
 39. The one or morenon-transitory computer readable media of claim 28, wherein the firstaudiovisual content comprises television programming.
 40. The one ormore non-transitory computer readable media of claim 28, wherein thedescriptive information associated with the first audiovisual contentcomprises optical character recognition information.
 41. The one or morenon-transitory computer readable media of claim 28, wherein the firstweight is higher than the second weight.
 42. The one or morenon-transitory computer readable media of claim 28, wherein a firstrelationship of the one or more relationships comprises a parent-childrelationship between the one or more first words and the one or moresecond words, and wherein the first weight is lower than the secondweight.
 43. The one or more non-transitory computer readable media ofclaim 28, wherein a first relationship of the one or more relationshipscomprises a sibling relationship between the one or more first words andthe one or more second words, and wherein the first weight is lower thanthe second weight.
 44. The one or more non-transitory computer readablemedia of claim 28, wherein the instructions, when executed, cause thedetermining the one or more first words by causing determining, based ona closed caption stream associated with the first audiovisual content,the one or more first words.
 45. The one or more non-transitory computerreadable media of claim 28, wherein the instructions, when executed,cause the determining the one or more first words by causing performingvideo analytics on the first audiovisual content.
 46. The one or morenon-transitory computer readable media of claim 28, wherein theinstructions, when executed, cause the determining the one or more firstwords by causing determining, based on metadata associated with thefirst audiovisual content, the one or more first words.
 47. One or morenon-transitory computer readable media storing instructions that, whenexecuted, cause: determining, based on first audiovisual content beingoutput for display, one or more first words; determining one or moresecond words based on: the one or more first words; and one or morerelationships that associate at least one of the one or more first wordswith the one or more second words; determining search information thatis based on the one or more first words and the one or more secondwords; determining, based on the search information, second audiovisualcontent; and causing display of information associated with the secondaudiovisual content.
 48. The one or more non-transitory computerreadable media of claim 47, wherein the instructions, when executed,cause the determining the one or more first words by causingdetermining, based on receipt of a signal indicating a user selection ofa search function, the one or more first words.
 49. The one or morenon-transitory computer readable media of claim 47, wherein theinstructions, when executed, cause the determining the one or more firstwords by causing performing facial recognition or voice recognition onthe first audiovisual content.
 50. The one or more non-transitorycomputer readable media of claim 47, wherein a first relationship of theone or more relationships comprises a parent-child relationship betweenthe one or more first words and the one or more second words, andwherein the instructions, when executed, cause: determining a firstweight associated with the one or more first words and a second weightassociated with the one or more second words, and wherein a differencebetween the first weight and the second weight is dependent on theparent-child relationship.
 51. The one or more non-transitory computerreadable media of claim 50, wherein the second weight is based on adegree of relatedness, in a knowledge domain, between the one or morefirst words and the one or more second words.
 52. The one or morenon-transitory computer readable media of claim 47, wherein theinstructions, when executed, cause: analyzing audio of the firstaudiovisual content, analyzing video of the first audiovisual content,or analyzing metadata associated with the first audiovisual content. 53.The one or more non-transitory computer readable media of claim 47,wherein the one or more relationships associate the one or more secondwords as a subcategory of the one or more first words.
 54. The one ormore non-transitory computer readable media of claim 47, wherein the oneor more relationships comprise a sibling relationship between the one ormore first words and the one or more second words.